Vehicle Travel Research Articles

Energy management in integrated energy system with electric vehicles as mobile energy storage: An approach using bi-level deep reinforcement learning

The integrated energy system with electric vehicle charging station via vehicle-to-grid aims to offer a proactive solution for low-carbon development of both energy and transportation sectors. However, achieving optimal energy efficiency with minimal operational costs in such a complex system is challenging due to the high randomness of electric vehicle travel patterns. This work proposes a reinforcement learning-based energy management framework to optimize the coordinated scheduling of integrated energy and vehicle-to-grid. The problem is modeled as a Markov Decision Process and optimized using the bi-level soft actor-critic algorithm, where the upper-level agent supervises the scheduling of the integrated energy system, and lower-level agent sets the electric vehicles’ charging and discharging strategies based on upper-level decision. Numerical simulations demonstrated that by adopting a bi-level reinforcement learning approach, the proposed algorithm effectively enhances energy exchange between integrated energy and electric vehicle charging station, reducing operational costs by 8 % compared to other multi-agent algorithms. Furthermore, when compared to the Mixed-Integer Linear Programming model, the proposed algorithm achieves similar optimality with significantly less computation time, maintaining a margin of error of approximately 4 %. Additionally, integrating electric vehicles as mobile energy storage within this framework can lead to a further 10 % reduction in operating costs.

Impacts of brightness contrast, road environment complexity, travel direction and judgement type on speed perception errors among older adult pedestrians' road-crossing decision-making.

This study aimed to explore how various factors affect older people's vehicle speed perception to enhance their road safety as pedestrians, focusing on the impact of their cognitive and perceptual abilities on road-crossing decisions. The study evaluated the effects of brightness contrast (high, medium and low), road complexity (high and low) and vehicle travel direction (same and opposite) on speed perception errors in simulated traffic settings. It involved 38 older participants who estimated the speed of a comparison vehicle under two judgement conditions. Findings showed a consistent underestimation of speed in all conditions. A repeated-measure ANOVA revealed that speed perception errors were significantly higher with low brightness contrast, in simpler road environments, with vehicles travelling in the same direction, and when using absolute judgements. These results have practical importance for public safety initiatives, traffic regulation and road design catering to older adults' perceptual needs. They also provide valuable insights for driver training programs for older adults, aimed at enhancing their understanding and management of perceptual biases.

A Pathfinding Algorithm for Large-Scale Complex Terrain Environments in the Field

Pathfinding for autonomous vehicles in large-scale complex terrain environments is difficult when aiming to balance efficiency and quality. To solve the problem, this paper proposes Hierarchical Path-Finding A* based on Multi-Scale Rectangle, called RHA*, which achieves efficient pathfinding and high path quality for large-scale unequal-weighted maps. Firstly, the original map grid cells were aggregated into fixed-size clusters. Then, an abstract map was constructed by aggregating equal-weighted clusters into rectangular regions of different sizes and calculating the nodes and edges of the regions in advance. Finally, real-time pathfinding was performed based on the abstract map. The experiment showed that the computation time of real-time pathfinding was reduced by 96.64% compared to A* and 20.38% compared to HPA*. The total cost of the generated path deviated no more than 0.05% compared to A*. The deviation value is reduced by 99.2% compared to HPA*. The generated path can be used for autonomous vehicle traveling in off-road environments.

Multi-strategy cooperative scheduling for airport specialized vehicles based on digital twins

Efficient specialized vehicle cooperative scheduling is significant for airport operations, particularly during times of high traffic, which reduces the risk of flight delays and increases customer satisfaction. In this paper,we construct a multi-type vehicles collaborative scheduling model with the objectives of minimizing vehicle travel distance and vehicle waiting time. Additionally, a three-layer genetic algorithm is designed, and the crossover and mutation operations are enhanced to address the scheduling model. Due to the numerous uncertainties and stochastic interferences in airport operations, conventional scheduling methods unable to effectively address these challenges, this paper combines improved genetic algorithm, simulation algorithm, and digital twins technology, proposing a multi-strategy scheduling framework for specialized vehicles based on digital twins. The scheduling framework utilises digital twins to capture dynamic data from the airport and continuously adjusts the scheduling plan through the scheduling strategy to ensure robust scheduling for specialized vehicles. In the event of severe delays at the airport, fast and efficient re-scheduling can be achieved. Finally, the effectiveness of the proposed scheduling framework is validated using domestic flight data, and extensive experiments and analyses are conducted in different scenarios. This research contributes to addressing the optimization problem of cooperative scheduling for multi-type vehicles at airports.

Optimization Model for Relief Distribution After Flood Disaster

Logistics planning is critical and a key component in meeting initial emergency needs in the aftermath of a disaster. The rapid and efficient distribution of logistical aid becomes critically important. In such situations, the construction of temporary depots in strategic locations and the determination of optimal distribution routes play an important role in ensuring that logistics aid can be distributed to the affected areas evenly. In this study, the Multi Depot Vehicle Routing Problem (MDVRP) is used which aims to minimize the total cost of distributing logistics aid which includes shipping costs, vehicle usage costs, temporary depot construction costs, and vehicle travel costs from distribution centers to temporary depots, while still meeting constraints such as logistics aid demand, vehicle capacity, area visits, maximum mileage, and depot construction. This model uses two types of vehicles where vehicle is tasked with carrying logistics aid from the distribution center to the temporary depot and vehicle is tasked with delivering logistics aid directly to the point of demand.

Fault-Tolerant Control Study of Four-Wheel Independent Drive Electric Vehicles Based on Drive Actuator Faults

Failure of any of the drive systems in a Four-Wheel Independent Drive (4WID) electric vehicle may affect the control performance and driving safety of the whole vehicle. Therefore, in this paper, a fault-tolerant controller (FTC) for 4WID electric vehicles considering drive actuator failures is proposed. First, a comprehensive characterization of multiple fault types is achieved by establishing a generalized fault model and designing a comprehensive fault factor. Second, based on the comprehensive fault factor, an LPV model with faults is constructed. Further, a fault-tolerant controller based on LPV/H∞ output feedback is designed by combining the weighting function. Finally, the effectiveness of the FTC in this paper is verified by simulation and hardware-in-the-loop (HIL) experiments. The experimental results show that the FTC designed in this paper can improve the stability of the vehicle traveling while ensuring tracking accuracy when the drive system fails.

Expressway Vehicle Arrival Time Estimation Algorithm Based on Electronic Toll Collection Data

Precise travel time prediction benefits travelers and traffic managers by enabling anticipation of future roadway conditions, thus aiding in pre-trip planning and the development of traffic control strategies. This approach contributes to reducing travel time and alleviating traffic congestion issues. To achieve real-time state perception of vehicles on expressways, we propose an algorithm to estimate the arrival time of vehicles in the next segment using Electronic Toll Collection (ETC) data. Firstly, the characteristics of ETC data and GPS data are meticulously described. We devise algorithms for data cleaning and fusion, subsequently segmenting the vehicle journey into multiple sub-segments. In the following step, feature vectors are constructed from the fused data to detect service areas and analyze the expressway segment characteristics, vehicle traits, and the influence of service areas. Finally, an algorithm utilizing LightGBM is introduced for estimating the arrival time of vehicles at various segments, corroborated by empirical tests using authentic traffic data. The MAE of the algorithm is recorded as 20.1 s, with an RMSE of 32.6 s, affirming its efficacy. The method proposed in this paper can help optimize transportation systems for improving efficiency, alleviating congestion, reducing emissions, and enhancing safety.

Prediction of Damage and Remaining Life of Yogyakarta-Bawen Toll Road Pavement with Elastic and Viscoelastic Approaches

Toll roads are one of the infrastructure development targets in Indonesia to speed up traffic and vehicle travel times. This paper analyzes the flexible pavement structure of the Yogyakarta-Bawen Toll Road. It predicts damage and residual values using elastic and viscoelastic approaches based on MDPJ Bina Marga 2017 using the KENPAVE Program. The recommended design alternative is pavement with a layer of AC-WC 40 mm, AC-BC 60 mm, Base AC 180 mm, and LPA 300 mm. This alternative is predicted to be able to serve load repetitions in the elastic approach of 432,857,828 ESAL until fatigue cracking occurs, 215,176,089 ESAL until rutting occurs, and 136,575,580 ESAL until permanent deformation occurs. Analysis using the viscoelastic approach can provide load repetitions of 153,304,378 ESAL until fatigue cracking occurs, 47,014,109 ESAL until rutting occurs, and 71,859,938 ESAL until permanent deformation occurs. Based on the elastic approach, the pavement's service life is 33 years, with initial damage in the form of permanent deformation. In comparison, the viscoelastic approach has a service life of 22.7 years, with initial damage in the form of rutting.

An intelligent FL-based vehicle route optimization protocol for green and sustainable IoT connected IoV

The intelligent Internet of Vehicles (IoV) provides superior results in effectively addressing complex transportation challenges. Predicting vehicle traffic, crashes, demand, location, communication, and travel safety are all critical issues in today’s transportation systems. The proposed paper optimizes vehicle traffic by incorporating reroute recommendations, increasing the use of public transportation, and providing onboard vehicle drivers with intelligent health assistance using Federated Learning (FL). This research also focuses on resolving complex transportation issues such as a vehicle’s current location, exact vehicle count information on each route, and onboard vehicle vacant seat information. Furthermore, vehicle communication contributes to the proposed system’s efficiency by avoiding communication delays or information loss to registered users and the cloud server. An intelligent FL-based scheme for vehicle route optimization has been proposed as part of this research to prevent vehicle traffic in a real-time Internet of Things (IoT) connected IoV transportation system. The vehicle detection approach determines the number of vehicles traveling on each route to recommend the best route to registered users. The effective implementation of cluster-based vehicle communication and location estimation models enhances the efficiency of the proposed system.

Baseline vulnerable road user injury risk in multiple U.S. dense urban driving environments

Objective Understanding and modeling baseline driving safety risk in dense urban areas represents a crucial starting point for automated driving system (ADS) safety impact analysis. The purpose of this study was to leverage naturalistic vulnerable road user (VRU) collision data to quantify collision rates, crash severity, and injury risk distributions in the absence of objective injury outcome data. Methods From over 500 million vehicle miles traveled, a total of 335 collision events involving VRUs were video verified and reconstructed (126 pedestrians, 144 cyclists, and 65 motorcyclists). Data consisted of anonymized video and sensor data (Global Positioning System and accelerometer) from vehicles equipped with Nexar dash cameras. Each event was qualitatively evaluated to assess the collision geometries and other extrinsic collision factors (e.g., time of day, roadway location, presence of occlusions). Previously published injury risk models were utilized to provide estimates of Maximum Abbreviated Injury Scale MAIS2+ and MAIS3+ injury potential. Collision severity distributions and aggregated injury risk estimates were compared. Results Vehicle speeds at the time of impact ranged from 0 to 39 mph, and VRU speeds ranged from 0 to 27 mph. In general, vehicles were traveling at lower speeds at the time of impact when turning in comparison to straight travel. Nearly all events (95%) were associated with MAIS3+ injury risk estimates below 10%. Collisions involving a potential occlusion or the vehicle responding to surprising behavior by the VRU were associated with higher estimates of injury risk than those without occlusion or with the vehicle initiating the conflict. Based on accumulated risk for each event, it can be estimated that approximately 55 persons could be moderately injured (MAIS2+) and approximately 6 persons could be seriously injured (MAIS3+). Conclusion These data indicate that responding to surprising VRU behavior, having visibility be potentially occluded, and vehicle travel behavior were associated with differences in collision speed and injury risk estimation. The specific comparisons made in this study are not intended to be comprehensive but serve as a starting point for considering baseline driving risk associated with VRU collisions in dense urban areas.

Analysis of the Covid-19 pandemic on preferences of transport modes

The Covid-19 pandemic has affected the entire world and changed many aspects of daily life, including transportation behaviour and preferences. This study examines the situation of transportation behaviour and preferences before and after the Covid-19 pandemic process through a survey study. A total of 471 people participated in the survey, and 30 questions were posed to participants regarding transportation modes preference, daily usage habits, public transportation usage rate, income status, perceived risk level of transportation systems, preferences before and after the pandemic process, and more. The answers were classified, evaluated and statistically analysed. The results showed that users were significantly affected by the Covid-19 process and changed their transportation mode preferences. The use of buses in urban journeys decreased, and the use of private vehicles significantly increased after the Covid-19 process. Hygiene was the key factor in travel, followed by the vehicle occupancy rate, ventilation, fare and travel time factors. The perceived risk levels of public transportation systems were ranked as metrobus, minibus (paratransit), bus, metro, tram and ferry. The study suggests that future transportation system designs should consider user preferences, as well as changes in education and working conditions, and pandemic conditions.

A Road Crack Segmentation Method Based on Transformer and Multi-Scale Feature Fusion

To ensure the safety of vehicle travel, the maintenance of road infrastructure has become increasingly critical, with efficient and accurate detection techniques for road cracks emerging as a key research focus in the industry. The development of deep learning technologies has shown tremendous potential in improving the efficiency of road crack detection. While convolutional neural networks have proven effective in most semantic segmentation tasks, overcoming their limitations in road crack segmentation remains a challenge. To address this, this paper proposes a novel road crack segmentation network that leverages the powerful spatial feature modeling capabilities of Swin Transformer and the Encoder–Decoder architecture of DeepLabv3+. Additionally, the incorporation of a multi-scale coding module and attention mechanism enhances the network’s ability to densely fuse multi-scale features and expand the receptive field, thereby improving the integration of information from feature maps. Performance comparisons with current mainstream semantic segmentation models on crack datasets demonstrate that the proposed model achieves the best results, with an MIoU of 81.06%, Precision of 79.95%, and F1-score of 77.56%. The experimental results further highlight the model’s superior ability in identifying complex and irregular cracks and extracting contours, providing guidance for future applications in this field.

Research on the Control Strategy of the Power Shift System of a Cotton Picker Based on a Fuzzy Algorithm

The control strategy of a power shift system has a significant impact on the driving stability and comfort of cotton pickers. To prevent the cotton picker from stopping to shift and reduce the jerks while shifting, in this study, the power shift system of a four-speed cotton picker was taken as the research object, and the working principles of the power shift transmission of the cotton picker and the hydrostatic transmission were analyzed. Firstly, the intelligent fuzzy control strategy of the variable pump and dual variable motor displacement of the cotton picker power shift system was designed using a fuzzy algorithm, and two-input and three-output fuzzy controllers were constructed with the engine rotational speed and vehicle speed as the input parameters, and the variable pump displacement, front motor displacement, and rear motor displacement as the outputs. Secondly, the model of the whole vehicle travel drive system of the cotton picker was constructed using co-simulation of Amesim and Simulink. Finally, the influence of the fuzzy shift control strategy and conventional manual shift method on the speed and driving distance of the cotton picker was compared and analyzed. The analysis results show that the fuzzy algorithm-based control strategy of the power shift system of the cotton picker can ensure that the cotton picker travels stably according to the target speed, and effectively reduces the speed fluctuation and jerks while shifting. The results of the study are of great significance to realize the non-stop shifting of the cotton picker as well as improve the stability and smoothness of the shift while driving.

The effects of vehicle color and travel direction on perceived speed error varies by judgment type among older pedestrians

Objective Misjudgments of vehicle speed or distance frequently lead to collisions, particularly among older pedestrians who are less accurate in estimating vehicle speeds than younger individuals. However, comprehensive studies that assess multiple factors influencing speed perception in older pedestrians are lacking. Methods This research utilized computer simulations to explore how vehicle color (red, green, blue) and direction of travel (approaching or receding) affect perceived speed errors in both relative and absolute judgment scenarios among older pedestrians. Results Data from 38 older adults and 40 college students indicated that red vehicles were associated with fewer perceived speed errors than either green or blue vehicles. Errors increased for vehicles moving away, with absolute judgments showing greater discrepancies than relative ones. Analysis revealed that, across various combinations of the three independent variables—vehicle color, vehicle direction, and judgment type—the older participants exhibited significantly larger perceived speed errors compared to college students. Furthermore, the study identified significant interactions between vehicle color and direction, and between judgment type and vehicle direction. Conclusion Our findings are beneficial in understanding the factors influencing older pedestrians’ speed perceptions, aiding public safety and informing car design to ensure safer roads for older pedestrians.

Locating carbon neutral mobility hubs using artificial intelligence techniques

This research proposes a novel, three-tier AI-based scheme for the allocation of carbon–neutral mobility hubs. Initially, it identified optimal sites using a genetic algorithm, which optimized travel times and achieved a high fitness value of 77,000,000. Second, it involved an Ensemble-based suitability analysis of the pinpointed locations, using factors such as land use mix, densities of population and employment, and proximities of parking, biking, and transit. Each factor is weighted by its carbon emissions contribution, then incorporated into a suitability analysis model, generating scores that guide the final selection of the most suitable mobility hub sites. The final step employs a traffic assignment model to evaluate these sites’ environmental and economic impacts. This includes measuring reductions in vehicle kilometers traveled and calculating other cost savings. Focusing on addressing sustainable development goals 11 and 9, this study leverages advanced techniques to enhance transportation planning policies. The Ensemble model demonstrated strong predictive accuracy, achieving an R-squared of 95% in training and 53% in testing. The identified hubs’ sites reduced daily vehicle travel by 771,074 km, leading to annual savings of 225.5 million USD. This comprehensive approach integrates carbon-focused analyses and post-assessment evaluations, thereby offering a comprehensive framework for sustainable mobility hub planning.

Low-Cost Portable Road Smoothness Testing Method Based on Pseudo-Vibration Velocity Range

Road smoothness not only directly affects the safety and comfort of vehicle travel but also relates to the efficiency and cost-effectiveness of road maintenance. Traditional road smoothness detection methods usually require professional equipment and personnel, leading to high costs and cumbersome operations. Therefore, finding a low-cost, simple, and accurate method for detecting road smoothness is of great significance. This study uses vehicle-mounted acceleration sensors to detect road smoothness, establishing a correlation between driving vibration acceleration data and the international roughness index (IRI). For this research, a driving vibration acceleration data acquisition device was developed, and the driving acceleration data from the test sections were denoised and their feature values extracted. The pseudo-vibration velocity range was used as the characteristic index representing the road surface smoothness IRI value. Testing with different vehicle types showed that the method is applicable to both sedans and SUV models, yielding a relative error of 8.9% for the sedan smoothness test model and 6.7% for the SUV smoothness test model. This study contributes to conducting large-scale road smoothness detection at a low cost, improving the efficiency of road maintenance and operations.

Next track point prediction using a flexible strategy of subgraph learning on road networks

Accurately predicting the next track point of vehicle travel is crucial for various Intelligent Transportation System (ITS) applications, such as travel behavior studies, traffic control, and traffic congestion monitoring. Recent works on trajectory prediction follow a paradigm that first represents the raw trajectory and subsequently makes predictions based on that representation. Currently, trajectory representation methods tend to project trajectory points to road networks by map matching and represent trajectories based on the representation of matched roads. However, precisely matching trajectories to roads is a challenge in ITS, as the matching precision is greatly affected by the quality of the trajectory. Meanwhile, since it is difficult to discern whether trajectory matching results are accurate or confounded, how to effectively utilize this type of uncertain geographic context information is also a challenge, which is defined as the Uncertain Geographic Context Problem (UGCoP) in geographic information science. Therefore, we propose a flexible strategy of subgraph learning, referred to as SLM, for predicting the next track point of vehicles. Specifically, a subgraph generation module is first proposed to extract topology contextual information of the roads around historical trajectory points. Secondly, a subgraph learning module is designed to learn rich spatial and temporal features from generated subgraphs. Finally, the extracted spatiotemporal features will be fed into a prediction module to predict the next track points of vehicles on road networks. Our model enables the effective utilization of uncertain geographic context information of trajectories on road networks while avoiding the error brought by map matching. Extensive experiments based on trajectory datasets in two different cities confirm the effectiveness of our approach.

Route optimization of battery electric vehicles using dynamic charging on electrified roads

The Electric Road System (ERS) is an innovative technology enabling electric vehicles (EVs) to charge while in motion, offering increased EV range and cost-effective battery solutions. This advancement is particularly valuable for logistics companies seeking to enhance their EV adoption. To provide long-distance delivery routes for heavy-duty EVs through ERSs, we present a first-time attempt to solve the Electric Vehicle Travelling Salesman Problem with Dynamic Charging, where the EV can be charged while driving on the electrified road segments. We formulate this problem by considering path choices between consecutive customers and propose a network reduction heuristic (NRH) to solve large-size instances. We test our model and NRH on grid-like synthetic instances and real-world data developed from the UK road network. Our results demonstrate the effectiveness of the NRH and highlight the potential of ERSs in improving logistics activities and decarbonising road transport. Our experiments revealed that although the typical driving range of most EVs falls short of 400 kilometres, with the widespread deployment of ERS, EVs can traverse distances exceeding 2000 kilometres without requiring recharging stops. Our experiments also revealed that establishing infrastructure on approximately 30 % to 40 % of all roadways is adequate to ensure reliable access for EVs.

An Integration of Real-Time Vehicle Routing and Mobile Technology in Poultry Distribution

Handling the distribution of broilers has special attention. The distribution of poultry should be done in the shortest possible time to decrease broiler mortality rate. Real-time monitoring of poultry distribution is expected to control the stress level of poultry due to the length of travel time by limiting the delivery time window. In order to control the distribution time and distance, it is necessary to integrate vehicle routing and mobile technology. By adopting the digital twin-enabled framework model and combining it with Google APIs, web and mobile applications are generated to accommodate the real-time traveling time, vehicle capacity, and pickup-delivery activity. There is a significant change in travel distance in one delivery cycle after implementing the developed technology. The total travel distance was 493 KM decreased to 436 KM, which means a decrease of 11.5% from the initial total travel distance. In addition, there was a change in the total penalty time (delay) from 102 minutes to 85 minutes, decreasing delay time by 16.6%. This mobile technology's development can indirectly increase vehicle utilization by not overloading the vehicle capacity and reducing vehicle travel distance.

Predicting vehicle travel time on city streets for trip preplanning and predicting heavy traffic for proactive control of street congestion

We investigate if the vehicle travel time after 6 h on a given street can be predicted, provided the hourly vehicle travel time on the street in the last 19 h. Likewise, we examine if the traffic status (i.e., low, mild, or high) after 6 h on a given street can be predicted, provided the hourly traffic status of the street in the last 19 h. To pursue our objectives, we exploited historical hourly traffic data from Google Maps for a main street in the capital city of Jordan, Amman. We employ several machine learning algorithms to construct our predictive models: neural networks, gradient boosting, support vector machines, AdaBoost, and nearest neighbors. Our experimental results confirm our investigations positively, such that our models have an accuracy of around 98–99% in predicting vehicle travel time and traffic status on our study’s street for the target hour (i.e., after 6 h from a specific point in time). Moreover, given our time series traffic data and our constructed predictive models, we inspect the most critical indicators of street traffic status and vehicle travel time after 6 h on our study’s street. However, as we elaborate in the article, our predictive models do not agree on the degree of importance of our data features.